Indications and expectations for neuropsychological assessment in epilepsy surgery in children and adults
Bibliographic record
Abstract
In our first paper in this series (Epilepsia 2015; 56(5): 674-681), we published recommendations for the indications and expectations for neuropsychological assessment in routine epilepsy care. This partner paper provides a comprehensive overview of the more specialist role of neuropsychological assessment in the pre and postoperative evaluation of epilepsy surgery patients. The paper is in two parts. The first part presents the framework for the mandatory role of neuropsychologists in the presurgical evaluation of epilepsy surgery candidates. A preoperative neuropsychological assessment should be comprised of standardised measures of cognitive function in addition to wider measures of behavioural and psychosocial function. The results from the presurgical assessment are used to: (1) establish a baseline against which change can be measured following surgery; (2) provide a collaborative contribution to seizure characterization, lateralization and localization; (3) provide evidence-based predictions of cognitive risk associated with the proposed surgery; and (4) provide the evidence base for comprehensive preoperative counselling, including exploration of patient expectations of surgical treatment. The second part examines the critical role of the neuropsychologist in the evaluation of postoperative outcomes. Neuropsychological changes following surgery are dynamic and a comprehensive, long-term assessment of these changes following surgery should form an integral part of the postoperative follow-up. The special considerations with respect to pre and postoperative assessment when working with paediatric populations and those with an intellectual disability are also discussed. The paper provides a summary checklist for neuropsychological involvement throughout the epilepsy surgery process, based on the recommendations discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".